Applied Machine Learning Method to Predict Children With ADHD Using Prefrontal Cortex Activity: A Multicenter Study in Japan

Yasumura, Akira; Omori, Mikimasa; Fukuda, Ayako; Takahashi, Jutaro; Yasumura, Yukiko; Nakagawa, Eiji; Koike, Takao; Yamashita, Yushiro; Miyajima, Tasuku; Koeda, Tatsuya; Aihara, Masao; Tachimori, Hisateru; Inagaki, Masumi

doi:10.1177/1087054717740632

Cited by 39 publications

(61 citation statements)

References 27 publications

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“…Comparing our results to other ML methods previously used to discriminate between ADHD and controls suggests that an ML model based on CPT data holds the promise of discriminating children with ADHD from controls, even when compared to more invasive or expensive approaches. For example, Yasumura et al ( 2017 ), who used near-infrared spectroscopy to quantify the change in prefrontal cortex oxygenated hemoglobin during reversed Stroop task, found an overall discrimination rate of 86.25%, with a sensitivity of 88.71% and a specificity of 83.78%. Likewise, using MRI data, Peng et al ( 2013 ) found an ADHD prediction accuracy of 90.18%.…”

Section: Discussionmentioning

confidence: 99%

“…Availability and affordability of data collecting devices have opened doors for the use of ML to predict the likelihood of individuals developing a set of mental disorders such as depression, anxiety, autism, dementia, brain tumors, schizophrenia, psychosis, et cetera (Sen et al, 2018 ; Sakai and Yamada, 2019 ; Vieira et al, 2019 ). A growing number of supervised ML studies have been carried out on discriminating ADHD from control groups using the data obtained from electroencephalogram (EEG; Tenev et al, 2014 ), brain structural magnetic resonance imaging (MRI; Peng et al, 2013 ), MRI and functional magnetic resonance imaging (fMRI; Sen et al, 2018 ), Near-infrared spectroscopy (NIRS; Yasumura et al, 2017 ), and a combination of subjective and objective measures of ADHD (Emser et al, 2018 ). ML was also used to predict methylphenidate response in youth with ADHD using environmental, genetic, neuroimaging, and neuropsychological data (Kim et al, 2015 ).…”

Section: Introductionmentioning

confidence: 99%

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A Machine-Based Prediction Model of ADHD Using CPT Data

Slobodin

Yahav

Berger

2020

Front. Hum. Neurosci.

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Despite the popularity of the continuous performance test (CPT) in the diagnosis of attention-deficit/hyperactivity disorder (ADHD), its specificity, sensitivity, and ecological validity are still debated. To address some of the known shortcomings of traditional analysis and interpretation of CPT data, the present study applied a machine learningbased model (ML) using CPT indices for the Prediction of ADHD.Using a retrospective factorial fitting, followed by a bootstrap technique, we trained, cross-validated, and tested learning models on CPT performance data of 458 children aged 6-12 years (213 children with ADHD and 245 typically developed children). We used the MOXO-CPT version that included visual and auditory stimuli distractors. Results showed that the ML proposed model performed better and had a higher accuracy than the benchmark approach that used clinical data only. Using the CPT total score (that included all four indices: Attention, Timeliness, Hyperactivity, and Impulsiveness), as well as four control variables [age, gender, day of the week (DoW), time of day (ToD)], provided the most salient information for discriminating children with ADHD from their typically developed peers. This model had an accuracy rate of 87%, a sensitivity rate of 89%, and a specificity rate of 84%. This performance was 34% higher than the best-achieved accuracy of the benchmark model. The ML detection model could classify children with ADHD with high accuracy based on CPT performance. ML model of ADHD holds the promise of enhancing, perhaps complementing, behavioral assessment and may be used as a supportive measure in the evaluation of ADHD.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Machine-Based Prediction Model of ADHD Using CPT Data

Slobodin

Yahav

Berger

2020

Front. Hum. Neurosci.

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“…Few studies have used fNIRS in psychiatric disorders in children and adolescents, although ADHD is the most studied disorder [11][12][13]. Studies using fNIRS have focused on the differ-ence in hemodynamic activity according to the symptoms of ADHD or the prediction of treatment response to medications through various cognitive tasks.…”

Section: Attention-deficit/ Hyperactivity Disordermentioning

confidence: 99%

“…First, the examination of the differences in hemodynamic activity according to symptoms is based on the following principle: the core symptoms of ADHD (inattention, hyperactivity, and impulsivity) are related to executive function impairment and mainly reflect the function of the prefrontal cortex (PFC) [14][15][16]. Among the executive functions, impaired inhibition is frequently observed in children with ADHD in fNIRS studies using the stroop task, reverse stroop task (RST), or Go/No-Go task [11][12][13]17]. Negoro et al [13] reported that children with ADHD (ADHD group) showed significantly smaller changes in oxy-hemoglobin than typically developing children (TDC group) in the inferior lateral PFC bilaterally during the Stroop color-word task.…”

Section: Attention-deficit/ Hyperactivity Disordermentioning

confidence: 99%

Clinical Applications of Functional Near-Infrared Spectroscopy in Children and Adolescents with Psychiatric Disorders

Lee

Kim

et al. 2021

J Korean Acad Child Adolesc Psychiatry

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The purpose of this review is to examine the clinical use of functional near-infrared spectroscopy (fNIRS) in children and adolescents with psychiatric disorders. Many studies have been conducted using objective evaluation tools for psychiatric evaluation, such as predicting psychiatric symptoms and treatment responses. Compared to other tools, fNIRS has the advantage of being a noninvasive, inexpensive, and portable method and can be used with patients in the awake state. This study mainly focused on its use in patients with attention-deficit/hyperactivity disorder and autism spectrum disorder. We hope that research involving fNIRS will be actively conducted in various diseases in the future.

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“…Compared to traditional parametrical models, multivariate machine learning techniques are able to leverage high dimensional information simultaneously to understand how variables jointly distinguish between groups (Greenstein et al, 2012). In literature, support vector machine (SVM) is the most frequently applied machine learning classifier in neuroimaging data from children with ADHD, which has been aided by recursive feature elimination (RFE), temporal averaging, principle component analysis (PCA), fast Fourier transform (FFT), independent component analysis (ICA), 10-fold cross-validation (CV), hold-out, and leave-one-out cross-validation (LOOCV) techniques, to distinguish children with ADHD from normal controls (Brown et al, 2012; Chang et al, 2012; Cheng et al, 2012; Colby et al, 2012; Du et al, 2016; Fair et al, 2012; Iannaccone et al, 2015; Johnston et al, 2014; Sen et al, 2018; Yasumura et al, 2017). The commonly reported most important features (according to importance score) that contribute to successful group discrimination included functional connectivity of bilateral thalamus, functional connectivity, surface area, cortical curvature and/or voxel intensity in frontal lobe, cingulate gyrus, temporal lobe, etc.…”

Section: Introductionmentioning

confidence: 99%

Multimodal Neuroimaging-based Prediction of Adult Outcomes in Childhood-onset ADHD using Ensemble Learning Techniques

Luo

Alvarez

Halperin

et al. 2019

Preprint

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Attention deficit/hyperactivity disorder (ADHD) is a highly prevalent and heterogeneous neurodevelopmental disorder, currently relaying on subjective symptom observations for diagnosis.Machine learning classifiers have been utilized to assist the development of neuroimaging-based biomarkers for objective diagnosis of ADHD. However, the existing basic model-based studies in ADHD reported suboptimal classification performances and inconclusive results, mainly due to the limited flexibility for each type of basic classifiers to appropriately handle multi-dimensional source features with various properties. In this study, we proposed to apply ensemble learning techniques (ELTs) in multimodal neuroimaging data collected from 72 young adults, including 36 probands (18 remitters and 18 persisters of childhood ADHD) and 36 group-matched controls. All the currently available optimization strategies for ELTs (i.e., voting, bagging, boosting and stacking techniques) were tested in a pool of semi-final classification results generated by seven basic classifiers. The highdimensional neuroimaging features for classification included regional cortical gray matter (GM) thickness and surface area, GM volume of subcortical structures, volume and fractional anisotropy of major white matter fiber tracts, pair-wise regional connectivity and global/nodal topological properties of the functional brain network for cue-evoked attention process. As a result, the bagging-based ELT with the base model of support vector machine achieved the best results, with the most significant improvement of the area under the receiver of operating characteristic curve (0.89 for ADHD vs. controls, and 0.9 for ADHD persisters vs. remitters). We found that features of nodal efficiency in right inferior frontal gyrus, right middle frontal (MFG)-inferior parietal (IPL) functional connectivity, and right amygdala volume significantly contributed to accurate discrimination between ADHD probands and controls; higher nodal efficiency of right MFG greatly contributed to inattentive and hyperactive/impulsive symptom remission, while higher right MFG-IPL functional connectivity strongly linked to symptom persistence in adults with childhood ADHD. Our study also suggested that considering their solidly improved robustness than the commonly implemented basic classifiers, ELTs may have the potential to identify more reliable neurobiological markers for severe brain disorders.structures estimated from structural MRI data, volume and FA of major WM fiber tracts derived from DTI data, the pair-wise regional connectivity and global/nodal topological properties (i.e., global-, local-, and nodal-efficiency, etc.) of the cue-evoked attention processing network computed from task-based fMRI data. Based on findings from existing studies from our group and other teams, we hypothesized that structural and functional alterations in frontal, parietal, and subcortical areas and their interactions would significantly contribute to accurate discrimination of ADHD probands (adults diag...

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Applied Machine Learning Method to Predict Children With ADHD Using Prefrontal Cortex Activity: A Multicenter Study in Japan

Abstract: A SVM using an objective index from RST may be useful as an auxiliary biomarker for diagnosis for children with ADHD.

Cited by 39 publications

References 27 publications

A Machine-Based Prediction Model of ADHD Using CPT Data

A Machine-Based Prediction Model of ADHD Using CPT Data

Clinical Applications of Functional Near-Infrared Spectroscopy in Children and Adolescents with Psychiatric Disorders

Multimodal Neuroimaging-based Prediction of Adult Outcomes in Childhood-onset ADHD using Ensemble Learning Techniques

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